Many organizations need to predict future events using large numbers of time series that are discretely valued. These time series, called “count series,” fall approximately between continuously-valued time series, for which there are many predictive techniques (ARIMA, UCM, ESM, and others), and intermittent time series, for which there are few predictive techniques (e.g., Croston's method). Most traditional time series analysis techniques assume that the time series values are continuously distributed. When a time series takes on small, discrete values (e.g., 0, 1, 2, 3, etc.), this assumption of continuity is unrealistic.